text: from generate_noisy_data import scan_directory
  import soundfile
  import numpy as np
  import time
  list_noisy_files = scan_directory('./Dataset/train/noisy_aug')
  
  def normalization(dataset):
      for i in range(len(dataset)):
          noisy_max = np.max(abs(dataset[i][0]))
          dataset[i][0] = dataset[i][0] / (noisy_max + 1e-7)
  
          clean_max = np.max(abs(dataset[i][1]))
          dataset[i][1] = dataset[i][1] / (clean_max + 1e-7)
      return dataset
  
  dataset = []
  batch = 0
  for noisy_addr in (list_noisy_files):
      st_time = time.time()
      batch += 1
      data_noisy, fs_noisy = soundfile.read(noisy_addr)
      clean_addr = str(noisy_addr).replace('noisy', 'clean')
  
      _lst = [] # '_'의 인덱스를 담을 리스트
      for idx, char in enumerate(clean_addr):
          if (char == '_'):
              _lst.append(idx) # '_'의 index를 추가함
  
      clean_addr = clean_addr[:_lst[9]] # clean 파일에 해당하는 name까지만 자름
      clean_addr += '.wav'
  
      data_clean, fs_clean = soundfile.read(clean_addr) # clean 데이터 읽기
      dataset.append([data_noisy, data_clean]) # clean / noisy 데이터 리스트에 추가
      print('{} done... takes {:.4} seconds...'.format(batch, time.time()-st_time))
  train_dataset = np.array(dataset)
  train_dataset = normalization(train_dataset)
  
  np.save('./Dataset/train_dataset_norm_tv31_snr51015_minus.npy',train_dataset)
  
  # load_data = np.load('./Dataset/train_dataset_norm.npy')
  # print(load_data.shape)
  